Timeline / Data.gov — Climate Datasets
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A new raw object was archived. Both versions are preserved. 892 line(s) added, 863 line(s) removed.
Evidence
| Source | Data.gov — Climate Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=climate&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-09-15T00:19:57+00:00 |
| Content type | application/json |
| Current object |
3c2be6c8bb0dba345abd7ccedd4c26d9dc5358791f354f675ad1a7f277b9e8cd
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| Previous object |
8f828f579488c82542c319f90f5ed9d06d8ea95cf1acce1887e55ee8f6828220
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What changed derived
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civic-memory.diff_engine 1.1.0 at
2026-09-15T00:19:57+00:00 by normalizing the two archived objects above. The
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without loss. 892 line(s) added, 863 line(s) removed.
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The SPEI dataset was developed using the SPEI package in R (Beguería & Vicente-Serrano, 2023). SPEI quantifies standardized departures in the balance between precipitation and potential evapotranspiration (PET) across varying timescales, making it highly suitable for assessing drought and water availability (Vicente-Serrano et al., 2010). Monthly precipitation and PET data were sourced from the MACAv2-METDATA dataset for climate projections between 1950-2099 based on 20 global climate models under RCP 4.5 and RCP 8.5 emission scenarios (Abatzoglou, 2013). Projected SPEI values were calculated relative to the 1981-2020 reference period, with SPEI computed using a log-logistic distribution fitted to the difference between precipitation and PET values. This methodology standardizes SPEI values as z-scores, allowing for comparative evaluations of drought and wetness across different regions and timescales (1 to 12 months).", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P1SV9SPJ", + "description": "Landing page for access to the data", + "format": "XML", + "mediaType": "application/http", + "title": "Digital Data" + }, + { + "@type": "dcat:Distribution", + "description": "The metadata original format", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.672bdd7bd34e16b32e739aff.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_672bdd7bd34e16b32e739aff", + "keyword": [ + "SPEI", + "USGS:672bdd7bd34e16b32e739aff", + "aridification", + "biota", + "climate change", + "climate projections", + "droughts", + "geospatial datasets", + "potential evapotranspiration", + "precipitation (atmospheric)" + ], + "modified": "2026-09-11T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-124.7388, 25.0631, -66.9287, 49.3960", + "theme": [ + "geospatial" + ], + "title": "Standardized Precipitation Evapotranspiration Index (SPEI) Projections for the Contiguous United States Based on the CMIP5 MACAv2-METDATA Downscaled Climate Dataset" + }, + "description": "The dataset consists of projections of 1-12 months Standardized Precipitation Evapotranspiration Index (SPEI) between 1950-2099 for the contiguous United States from 20 climate models and 2 emission scenarios at a 4km spatial resolution. The SPEI dataset was developed using the SPEI package in R (Beguería & Vicente-Serrano, 2023). SPEI quantifies standardized departures in the balance between precipitation and potential evapotranspiration (PET) across varying timescales, making it highly suitable for assessing drought and water availability (Vicente-Serrano et al., 2010). Monthly precipitation and PET data were sourced from the MACAv2-METDATA dataset for climate projections between 1950-2099 based on 20 global climate models under RCP 4.5 and RCP 8.5 emission scenarios (Abatzoglou, 2013). Projected SPEI values were calculated relative to the 1981-2020 reference period, with SPEI computed using a log-logistic distribution fitted to the difference between precipitation and PET values. This methodology standardizes SPEI values as z-scores, allowing for comparative evaluations of drought and wetness across different regions and timescales (1 to 12 months).", + "distribution_titles": [ + "Digital Data", + "Original Metadata" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/1d1b7df1-091c-4154-89a2-ca2da01c6f56", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/1d1b7df1-091c-4154-89a2-ca2da01c6f56/raw", + "has_download": true, + "has_spatial": true, + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_672bdd7bd34e16b32e739aff", + "keyword": [ + "SPEI", + "USGS:672bdd7bd34e16b32e739aff", + "aridification", + "biota", + "climate change", + "climate projections", + "droughts", + "geospatial datasets", + "potential evapotranspiration", + "precipitation (atmospheric)" + ], + "last_harvested_date": "2026-09-14T23:40:32.691133", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", + "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", + "name": "Department of the Interior", + "organization_type": "Federal Government", + "slug": "doi" + }, + "parent_identifier": null, + "popularity": 1, + "publisher": "U.S. Geological Survey", + "slug": "standardized-precipitation-evapotranspiration-index-spei-projections-for-the-contiguous-un", + "spatial_centroid": { + "lat": 34.796260000000004, + "lon": -101.61476 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + -124.7388, + 25.0631 + ], + [ + -124.7388, + 49.396 + ], + [ + -66.9287, + 49.396 + ], + [ + -66.9287, + 25.0631 + ], + [ + -124.7388, + 25.0631 + ] + ] + ], + "type": "Polygon" + }, + "theme": [ + "geospatial" + ], + "title": "Standardized Precipitation Evapotranspiration Index (SPEI) Projections for the Contiguous United States Based on the CMIP5 MACAv2-METDATA Downscaled Climate Dataset", + "type": "dataset" + }, + { + "_score": 55.476128, + "_sort": [ + 1789428810763, + 55.476128, + 3, + "d2aca380-a42e-4d93-9171-d20399b61875" + ], + "dcat": { + "accessLevel": "public", + "bureauCode": [ + "010:12" + ], + "contactPoint": { + "@type": "vcard:Contact", + "fn": "Graziella V. 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The species projection file has the results of the predicted probabilities of occurrence for each species of freshwater fish and mussel, at the HUC12 scale, for each of the seven scenarios: 1. baseline, 2. climate change, 3. climate change and dam removal, 4. climate change and natural riparian restoration, 5. climate change and riparian impervious removal, 6. climate change and watershed forest, and 7. climate change and a combination of numbers 3 through 6. The full methods for the fish models are described in the cited manuscript, Rogers et al., (2025); briefly, zero-inflated beta models (one for each fish species) were used to simultaneously model the probability of species occurrence and the predicted relative abundance of each fish species at the NHD Version 2 stream reach scale. The predicted probability of occurrence (from the binomial portion of the model) of each fish species at the stream reach level was averaged over the HUC12 scale to get the probability of occurrence of each of the 53 fish species by HUC12. The full methods for the mussel models are also described in Rogers et al., (In Prep); briefly we fit 12 logistic regression models (one for each mussel species) using the glm function in the stats package and the family of models set to ‘binomial’. The mussel models were fit using presence and absence data at the HUC12 scale and covariate data that was averaged across the HUC12. 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Freshwater fish and mussels survey records were used to fit species specific models that predict the probability of occurrence. The list of covariates is largely the same as the covariates released in the cross-referenced data set from Rogers et al., (2025); here, the covariate file shows the covariates used in this analysis that were not also used in the prior analysis. The species projection file has the results of the predicted probabilities of occurrence for each species of freshwater fish and mussel, at the HUC12 scale, for each of the seven scenarios: 1. baseline, 2. climate change, 3. climate change and dam removal, 4. climate change and natural riparian restoration, 5. climate change and riparian impervious removal, 6. climate change and watershed forest, and 7. climate change and a combination of numbers 3 through 6. The full methods for the fish models are described in the cited manuscript, Rogers et al., (2025); briefly, zero-inflated beta models (one for each fish species) were used to simultaneously model the probability of species occurrence and the predicted relative abundance of each fish species at the NHD Version 2 stream reach scale. The predicted probability of occurrence (from the binomial portion of the model) of each fish species at the stream reach level was averaged over the HUC12 scale to get the probability of occurrence of each of the 53 fish species by HUC12. The full methods for the mussel models are also described in Rogers et al., (In Prep); briefly we fit 12 logistic regression models (one for each mussel species) using the glm function in the stats package and the family of models set to ‘binomial’. The mussel models were fit using presence and absence data at the HUC12 scale and covariate data that was averaged across the HUC12. 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61.480587, + 61.479244, 1, "b4028bd6-f9f9-4552-b7cd-c5543206d498" ], @@ -4806,10 +5624,10 @@ "type": "dataset" }, { - "_score": 61.866264, + "_score": 61.869064, "_sort": [ 1789169939442, - 61.866264, + 61.869064, 1, "a453f06d-5ba6-4d4b-8422-83d553b285e5" ], @@ -4948,10 +5766,10 @@ "type": "dataset" }, { - "_score": 8.784348, + "_score": 8.785065, "_sort": [ 1789169938951, - 8.784348, + 8.785065, 0, "ca1a4168-9906-4b06-a718-babe0c805a1a" ], @@ -5007,793 +5825,4 @@ "theme": [ "geospatial" ], - "title": "Interview transcripts from meetings with coastal resource managers about data needs related to coastal wetlands and sea level rise" - }, - "description": "These three PDFs contain qualitative notes taken during focus group-style interviews in 2017 with coastal resource managers Grand Bay, AL; Port Aransas, TX; and Tampa Bay, FL about their data needs related to tidal wetlands and sea level rise and interest in working with USGS researchers to receive that data. The coastal managers were all engaged in conversations with USGS scientists as part of a separate project entitled Landscape conservation design for enhancing the adaptive capacity of coastal wetlands in the face of sea-level rise and coastal development, regarding tidal wetlands in the Gulf Coast region and the ability of investigators leading that project to provide data suitable for use in various resource management plans. Those investigators were leading a series of workshops across the Gulf Coast to introduce their work in an effort to make their research outputs more useful to regional decision makers. The current project had no role in identifying or selected coastal managers with whom to speak; that was the responsibility of the Landscape conservation design project and occurred before the involvement of the current project team. These data are particular to the interactions between the Landscape conservation design project team and the particular coastal managers who engaged with their project. 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WRF was run using boundary conditions from the ECHAM5 global model and the SRES A1B emissions scenario, one of the models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.\nThe second dataset, \"Western US Hydroclimate Scenarios Project Observations and Statistically Downscaled Data\", contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Note that time-stamps on these data are not in the future.", - "distribution": [ - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.55e4c25ce4b05561fa208552.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_55e4c25ce4b05561fa208552", - "keyword": [ - "Pacific Northwest", - "USGS:55e4c25ce4b05561fa208552", - "Western United States", - "change impacts", - "climate change", - "climatologyMeteorologyAtmosphere", - "dynamical downscaling", - "extremes", - "geospatial datasets", - "hydrologic change", - "statistical downscaling" - ], - "modified": "2026-09-09T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-126.5625, 33.1376, -102.6563, 48.4584", - "theme": [ - "geospatial" - ], - "title": "Western US Hydroclimate Scenarios Project Datasets" - }, - "description": "This archive contains two datasets. Both cover the northwestern United States and part of southern British Columbia (N of about 38 degrees N and W of about 105 degrees W) at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) in each are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references).\nThe first dataset, \"Western US Hydroclimate Scenarios Project Dynamically Downscaled Data\", contains daily dynamically downscaled climate projections and simulated land surface water and energy fluxes. The downscaling is based on the Weather Research and Forecasting (WRF) regional model. WRF was run using boundary conditions from the ECHAM5 global model and the SRES A1B emissions scenario, one of the models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.\nThe second dataset, \"Western US Hydroclimate Scenarios Project Observations and Statistically Downscaled Data\", contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Note that time-stamps on these data are not in the future.", - "distribution_titles": [ - "Original Metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/dd88c315-0068-4ef3-ae09-c8f503a040cf", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/dd88c315-0068-4ef3-ae09-c8f503a040cf/raw", - "has_download": true, - "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_55e4c25ce4b05561fa208552", - "keyword": [ - "Pacific Northwest", - "USGS:55e4c25ce4b05561fa208552", - "Western United States", - "change impacts", - "climate change", - "climatologyMeteorologyAtmosphere", - "dynamical downscaling", - "extremes", - "geospatial datasets", - "hydrologic change", - "statistical downscaling" - ], - "last_harvested_date": "2026-09-11T23:26:06.232324", - "organization": { - "aliases": [ - "dept" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", - "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", - "name": "Department of the Interior", - "organization_type": "Federal Government", - "slug": "doi" - }, - "parent_identifier": null, - "popularity": 0, - "publisher": "U.S. Geological Survey", - "slug": "western-us-hydroclimate-scenarios-project-datasets", - "spatial_centroid": { - "lat": 39.26592, - "lon": -117.00001999999999 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -126.5625, - 33.1376 - ], - [ - -126.5625, - 48.4584 - ], - [ - -102.6563, - 48.4584 - ], - [ - -102.6563, - 33.1376 - ], - [ - -126.5625, - 33.1376 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "Western US Hydroclimate Scenarios Project Datasets", - "type": "dataset" - }, - { - "_score": 32.863037, - "_sort": [ - 1789169075257, - 32.863037, - 0, - "79707b7c-f653-4661-bb5f-110e87610934" - ], - "dcat": { - "accessLevel": "public", - "bureauCode": [ - "010:12" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "Sarah L. Shafer", - "hasEmail": "mailto:sshafer@usgs.gov" - }, - "description": "Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions.We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0–58.0°N latitude by 136.6–103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070–2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not vis + "title": "Interview transcripts from meetings with coastal resource managers about data needs related to coastal wetlands and sea